---
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: classify-clickbait-titll
  results: []
---

# Identify Clickbait Articles

This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on a synthetic dataset with 65% factual article titles and 35% clickbait articles.

Built to demonstrate the use of synthetic data following, see the article [here](https://towardsdatascience.com/fine-tune-smaller-transformer-models-text-classification-77cbbd3bf02b). 

## Model description

Built to identify factual vs clickbait titles.

## Intended uses & limitations

Use it on any title to understand how the model is interpreting the title, whether it is factual or clickbait. 

Go ahead and try a few of your own. 

Here are a few examples:

**Title:** A Comprehensive Guide for Getting Started with Hugging Face
**Output:** Factual

**Title:** OpenAI GPT-4o: The New Best AI Model in the World. Like in the Movies. For Free
**Output:** Clickbait

**Title:** GPT4 Omni — So much more than just a voice assistant
**Output:** Clickbait

**Title:** Building Vector Databases with FastAPI and ChromaDB
**Output:** Factual

## Training and evaluation data

It achieves the following results on the evaluation set:
- Loss: 0.0173
- Accuracy: 0.9951
- F1: 0.9951
- Precision: 0.9951
- Recall: 0.9951
- Accuracy Label Clickbait: 0.9866
- Accuracy Label Factual: 1.0

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Framework versions

- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1